System &amp; method for predicting demand for items

ABSTRACT

A system and method for determining and identifying demand for items based on observing behavior of trendsetters within a member population, such as an online community. The trendsetters are determined by studying historical adoption behavior of a group within the member population.

RELATED APPLICATION DATA

[0001] The present application claims the benefit under 35 U.S.C. 119(e)of the priority date of Provisional Application Ser. No. 60/476,392filed Jun. 5, 2003, which is hereby incorporated by reference.

FIELD OF THE INVENTION

[0002] The present invention relates to identifying and measuring demandfor items based on identifying key trendsetter online users and adoptersof information, articles and services.

BACKGROUND

[0003] The Internet is used extensively now by a growing percentage ofthe public. At this time, several online websites in fact generate thebulk (if not the entirety) of their revenues from servicing online usersand subscribers. These include, for example, companies such as AOL andYahoo! (content providers), Amazon (books, music, and video recordings),EBay (auctions), Netflix (DVD rentals), Google (search engines) andDoubleclick (advertising) to name a few.

[0004] All of these companies monitor the interactions of online userswith their websites, and in some cases collect explicit profilinginformation as well from such users. This is done for the purpose ofcollecting both individualized and aggregate data, which in turn helpsthem to better customize the site and overall experience forsubscribers, to retain subscribers through personalized interactions, tobetter target advertising and product recommendations, etc. In someinstances the data is logged and later used for data mining purposes,such as for identifying trends (a specific example of this is describedin U.S. Pat. No. 6,493,703 which is hereby incorporated by reference)and for giving feedback to recommender systems (i.e. such as withNetflix's Cinematch engine).

[0005] A similar concept is illustrated in U.S. patent Publication No.2003/0004781 to Mallon et al. in which a community “buzz” index can beused to predict popularity, for example, of a particular movie before itis released. This application is also hereby incorporated by reference.Thus, this disclosure specifically mentions the usefulness of monitoringan overall awareness by an online group of certain concepts (i.e., suchas the brand name of a product), in order to gauge the potentialeconomic performance of such product later.

[0006] A website maintained by Yahoo!—buzz.yahoo.com—(the full URL isnot included because of PTO citation restrictions, but can be determinedby placing a browser executable suffix) also similarly monitors andtabulates online user content queries/viewings and identifies the samein a so-called “Buzz” score Index that is updated daily and presentedfor public viewing. This list, in essence, acts as a form of“popularity” identification for certain topics. For example, the listmay identify that stories about a particular singer were the most talkedabout, queried, or viewed.

[0007] The Buzz Index by Yahoo! further includes a “Movers” section,which basically identifies people, stories, etc., which experience thegreatest degree of change in buzz score on a day to day basis. Thus, forexample, a particular celebrity may be identified in a prominent story,and that would elevate such celebrity's “mover” status, even if theoverall buzz score was not sufficient to break into the top buzz scoreindex. For further information, the reader is recommended to suchwebsite.

[0008] Another related system used by Yahoo! is a marketing tool onanother website—solutions.yahoo.com—which permits companies to analyzebehavior of online users, and determine particular characteristics whichmay be useful to such company. For instance, in one case, Yahoo! wasable to track online behavior and combine it with traditionaldemographic and geographic information (to arrive at a subscriberprofile) for a company that provided moving services. From this data,they then tried to glean what profiling data was suggestive of a highlikelihood of such subscriber moving. In this manner, Yahoo! was able to“mine” the profiles and develop better target advertising for the movingcompany to a more specific audience. It can be seen that this examplecan be applied to many other fields.

[0009] While the aforementioned Yahoo! systems provide usefulinformation, they fail to yield at least one additional piece ofinformation: namely, which groups or subscribers are “trendsetters.” Inother words, while the Yahoo! Buzz Index identifies the existing toppopular concepts, and the concepts which are changing the most at anymoment in time, it makes no correlation between the two. That is, fromlooking at the Buzz Index Score for a particular concept, there is noway for a subscriber to know, which persons or group were the first tobe associated with such concept. Similarly, the marketing solutionswebsite is useful for predicting which persons are likely to meet aparticular criteria, but does not otherwise identify whether suchpersons are the first adopters of a particular concept—i.e., such as thefirst to query/view certain content, the first to buy a particularproduct, or the first to try a particular service.

[0010] This additional piece of information is extremely valuable,because it can be used in a variety of ways to improve an e-commercewebsite as explained in further detail below.

SUMMARY OF THE INVENTION

[0011] An object of the present invention, therefore, is to overcome theaforementioned limitations of the prior art;

[0012] Another object is to provide a system/method for identifyingtrendsetters, both within and outside an electronic community;

[0013] A related object is to provide a system/method for analyzing thebehavior and effects of trendsetters both within and outside anelectronic community;

[0014] Another object is to provide a system/method for analyzing thebehavior and effects of other members within and outside an electroniccommunity, including trend laggards, and trend rejecters;

[0015] Still another object is to provide a system/method for testing,rating and reporting on an adoption rate and/or expected demand for aparticular item, both within and outside an electronic community;

[0016] A further object is to provide an automated system/method forcustomizing and determining the effects of particular types ofadvertising on different types of members within an electroniccommunity;

[0017] Yet another object is to provide certain types of recommendersystems, search engines system, and a content presentation systems,which take into account the adoption behavior of participants using suchsystems;

[0018] Another object is to provide a system/method for calculating andquantifying the existence of trend predictor items within memberadoptions, which items are useful as markers for the potential successof other items within a member's list of adopted items.

[0019] A first aspect of the invention concerns a system and method ofidentifying trendsetters within an online community for items availableto members of the online community, the method comprises the steps of:identifying adoptions of a first item made by members of the onlinecommunity; ranking the adoptions of the first item to identify andprovide trendsetter ratings to members who are early adopters of theitem; and repeating steps (a) and,(b) for a plurality of second items.The early adopters of the first item can also be early adopters of oneor more of the plurality of second items. An ordered list of trendsettermembers based on the results of step (c) and the trendsetter ratings forthe first item and the plurality of second items is then generated. Theitems of interest can be a product, a service, content, a marketsecurity, etc.

[0020] In preferred embodiments, the trendsetter rating is scaled inaccordance with an adoption rank achieved by a member. Furthermore, thelist of trendsetter members can be published online for viewing by othermembers of the online community. The list of trendsetter members is usedto identify members entitled to incentives and/or rewards from anoperator of a website for the online community.

[0021] In some applications, members of the online community can electto subscribe to activities associated with selected trendsetter members,so that later adoptions by the selected trendsetter members arecommunicated to such members.

[0022] For other preferred embodiments, an additional step ofidentifying members who are trend predictors from the trendsettermembers is performed, as well as measuring a prevalence rate among thetrend predictors for a newly introduced item. In addition, a change inprevalence rate among the trendsetters for a newly introduced item canalso be measured, along with popularity prediction ratings for newlyintroduced items. The trendsetters can be members of a separate onlinecommunity.

[0023] In some embodiments, a recommendation is automatically generatedto a member of the online community for an item by examining items thatare prevalent among the trendsetter members.

[0024] In other embodiments, a set of profiles for the trendsettermembers can be provided to an e-commerce vendor and/or advertiser. Othersteps of monitoring online viewing, query and related interactionbehavior exhibited by members of the trendsetters can also be performed.

[0025] Another aspect of the invention concerns a method of identifyingtrend predictors within an online community. The method comprises thesteps of: identifying adoptions of a first item made by members of theonline community; ranking the adoptions of the first item to identifyand provide a trendsetter rating to members were early adopters of theitem; repeating steps (a) and (b) for a plurality of second items; theearly adopters of the first item can also be early adopters of one ormore of the plurality of second items; generating a list of trendpredictor members based on the results of step (c), the trendsettermembers being those members who score highest overall for a trendsetterrating across the entire online community for the first item and theplurality of second items. The trend predictor members are derived fromexamining the first K members who in aggregate first adopted all of aset of M separate popular items.

[0026] In preferred embodiments, the trend predictor members are used togenerate a prediction for a popularity of a newly introduced item. Theprediction is based on measuring a prevalence rate for the newlyintroduced item at a first time after introduction of such item. Theprediction is further based studying a change in the prevalence rate ata second time after the first time.

[0027] In other applications a further step of generating onlineadvertising tailored to the trend predictor members is performed. Inother embodiments, one or more proxy accounts are set up based on thetrend predictor members, to observe a behavior of one or more onlinewebsites towards the one or more proxy accounts.

[0028] Still another aspect of the invention concerns a method ofelectronically identifying trendsetters within a subscriber population.The method comprises the steps of: identifying a behavior and/or actionwhich is relatively prevalent within the subscriber population;identifying early adopters within the subscriber population of thebehavior and/or action by monitoring their activities during anelectronic data collection session; and repeating steps (a) and (b) tocompile a list of overall early adopters within the subscriberpopulation for a plurality of behaviors and/or actions.

[0029] The early adopters can be subscribers to content programmingservice, subscribers of a communications service provider, purchasers ofa software product, purchasers of a media product, such as a book, amovie, and/or a song, bidders in an electronic auction, purchasers ofone or more securities traded in a public market, consumers of contentpresented at an online content service provider, online participants inan electronic auction, etc.

[0030] Another aspect of the invention concerns a system with softwaremodules adapted to implement the methods above.

[0031] A related aspect of the invention concerns a method ofidentifying demand by an online community for a particular item, themethod comprising the steps of identifying early adopter members of theonline community, which early adopter members are characterized by ademand behavior for items which leads and is imitated by other membersof the online community; and measuring an acceptance value for theparticular item by such early adopter members of the online community.

[0032] In some embodiments, an additional step is performed ofgenerating a demand score identifying a predicted overall remainingdemand for the particular item. The demand score reflects a predictedoverall remaining demand for consumers of the particular item outside ofthe online community.

[0033] Still another aspect is directed to a method of identifyingdemand by an online community for a particular item, the methodcomprising the steps of: identifying late adopter members of the onlinecommunity, which late adopter members are characterized by a demandbehavior for the particular item which lags other members of the onlinecommunity; measuring an acceptance value for the particular item by suchlate adopter members of the online community; generating a demand scoreidentifying a predicted overall remaining demand for the particularitem. In some embodiments, a demand score reflects a predicted overallremaining demand for consumers of the particular item outside of theonline community.

[0034] Another aspect of the invention concerns a method of identifyingpredicted demand for an item, the method comprising the steps of:identifying a group of trend predictor members within an electroniconline community; identifying one or more items rejected by the group oftrend predictor members, by monitoring their activities duringelectronic data collection sessions; and compiling a list of the onemore rejected items.

[0035] For some applications, a further step is performed of identifyingtrend rejecter members within the electronic community, the trendrejecters being characterized as members who have adopted a substantialnumber of the one or more rejected items. An item rejection prevalencerate among the trend predictor members for each of the one or more itemsis also measured. In some embodiments, advertising can be disseminatedto the trend predictor members for at least one selected rejected itemfollowed by a step of measuring any change in an item rejectionprevalence rate for the at least one selected rejected item.

[0036] Another aspect of the invention concerns a method of identifyingpotential demand for a new product and/or new service within a market.The method comprises the steps of: identifying one or more productsand/or services which are characterized as having achieved substantialeconomic success within the market; identifying early adopters of theone or more products and/or services within an electronic community; andmonitoring the activities of the early adopters during electronic datacollection sessions, to determine a prevalence rating of theircollective impressions of the new product and/or new service.

[0037] For certain preferred embodiments, the last step is performedbefore the new product and/or new service is introduced into the market.In other embodiments, a further step of presenting advertising materialsto the early adopters is performed, as well as measuring a change in theprevalence rating.

[0038] Still another aspect of the invention concerns identifying trendpredictor items within a set of items available to an online community.This method comprises the steps of: identifying a first item having arelatively high adoption rate; calculating a correlation between thefirst item and one or more second items which have a relatively lowadoption rate; identifying at least one second item which is highlycorrelated to the first item, and not highly correlated to other of theone or more second items; repeating steps (b) and (c) to identify afirst set of first items which have relatively high adoption rates, anda second set of second items which are highly correlated to the firstset of first items. This method is useful for discovering interestingand hidden correlations between otherwise disparate items. In someapplications, a further step is performed of measuring a correlation ofthe second set of items to a newly introduced item to generate aprediction of a demand for the newly introduced item.

[0039] A related aspect of the invention concerns a system whichincludes software routines adapted to perform the above demandprediction methods. In some applications the system is embodied in anInternet based sever supporting a website maintained by an e-commerceoperator.

[0040] Another aspect of the invention concerns methods of influencingbehavior of an electronic recommender system. The method comprises thesteps of: identifying trendsetters, the trendsetters being individualswho are characterized as relatively early adopters of items that laterbecome relatively popular within the community of subscribers; measuringan adoption rate by the trendsetters for a particular item; andmodifying recommendations provided by the recommender system for items,including the particular item, based on a value of the adoption rate.

[0041] A further aspect of the invention is directed to a method ofinfluencing behavior of an electronic recommender system, comprising thesteps of: identifying trendsetters, the trendsetters being individualswho are characterized as relatively early adopters of items that laterbecome relatively popular within the community of subscribers; measuringa trendsetter rating for a particular item provided by the trendsetters;and modifying a user rating for the particular item for othersubscribers based on the trendsetter rating. The trendsetter rating isused as part of generating recommendations for items to a userrequesting a suggestion from the electronic recommender system. In someapplications the trendsetters are associated with web pages on theInternet.

[0042] A system for providing recommendations of items of interest to acommunity of online subscribers includes software modules for performingthe above steps.

[0043] A further aspect of the invention is directed to influencingbehavior of an electronic World Wide Web (WWW Internet search engine,which method comprises the steps of: compiling a set of trendsetter webpages, the set of trendsetter web pages being groups of one or more webpages characterized as relatively early adopters of items that laterbecome relatively popular within an electronic community; measuring atrendsetter rating for a first search item identified in the trendsetterweb pages; responding to query from a user for search items at least inpart based on the trendsetter rating for the first search item. Theitems can be uniform resource locators (URLs), electronic documents,hypertext links to electronic documents, products, services, multimediacontent, etc.

[0044] Still another aspect of the invention concerns a method ofclassifying World Wide Web pages, comprising the steps of: identifying aset of WWW pages; and extracting a set of early adopter WWW pages fromthe set of WWW pages, which set of early adopter WWW pages aredetermined by ranking the set of WWW pages in accordance with a time inwhich they first make a reference to a predetermined set of items.

[0045] A further aspect of the invention concerns a method ofinfluencing behavior of an electronic search engine system, which searchengine is used for providing results to queries initiated by a communityof online users of the World Wide Web, comprising the steps of:identifying trendsetter web pages, the trendsetter web pages beingcharacterized as relatively early presenters of content related to itemsthat later become relatively popular as measured by interestdemonstrated by online users for such items; measuring a trendsetter webpage rating for a particular item; and modifying a search resultprovided by the search engine to an online user based on the trendsetterweb page rating. The trendsetter web page rating is used, at leastduring certain time periods, for influencing which search results arepresented to a user query.

[0046] A related aspect of the invention concerns a method of presentingadvertising comprising the steps of: measuring an adoption behavior foritems exhibited by a first member of an online community during datacollection sessions; ranking the adoption behavior against adoptionbehavior of other members of the online community; and dynamicallyadjusting advertising presented to the first member during a lateronline session based on the adoption behavior.

[0047] Another aspect of the invention is directed do a method ofpresenting advertising to an online community, comprising the steps of:processing member historical records of content reviewed and/or itemspurchased for each member of the online community, and comparing suchmember historical records with other member historical records;identifying a first member as having a trendsetter status when theresults indicate that such first member exhibits behavior which isimitated by other members; interacting with the first member during anonline session; and adjusting advertising presented to the first memberduring the online session based on whether the first member has atrendsetter status.

[0048] Yet another aspect concerns a method of presenting advertising toan online community, comprising the steps of: processing memberhistorical records of content reviewed and/or items purchased for eachmember of the online community, and comparing such member historicalrecords with other member historical records; identifying a first memberas having a trendsetter status when the results indicate that such firstmember exhibits behavior which is imitated by other members; providing arecommendation to the first member within a first screen during anonline session using a recommender system; and adjusting advertisingpresented to the member in the first screen based on whether the firstmember has a trendsetter status.

[0049] It will be understood from the Detailed Description that theinventions can be implemented in a multitude of different embodiments.Furthermore, it will be readily appreciated by skilled artisans thatsuch different embodiments will likely include only one or more of theaforementioned objects of the present inventions. Thus, the absence ofone or more of such characteristics in any particular embodiment shouldnot be construed as limiting the scope of the present inventions.Furthermore, while the inventions are presented the context of certainexemplary embodiments, it will be apparent to those skilled in the artthat the present teachings could be used in any application where itwould be desirable and useful to identify the existence and behavior oftrendsetters.

DESCRIPTION OF THE DRAWINGS

[0050]FIG. 1 is a flow chart illustrating the steps performed by atrendsetter evaluation and feedback process implemented in accordancewith one exemplary embodiment of the present invention;

[0051]FIG. 2A is a flow chart illustrating the steps performed by atrendsetter identification process implemented in accordance with oneexemplary embodiment of the present invention;

[0052]FIG. 2B is a depiction of a portion of a trendsetter evaluationmatrix used by a trendsetter identification process implemented inaccordance with one exemplary embodiment of the present invention;

[0053]FIG. 2C illustrates a table generated by an exemplary calculationprocedure associated with the trendsetter evaluation matrix of FIG. 2B;

[0054]FIG. 3A illustrates the steps performed by an exemplary embodimentof the present invention to determine early adopters of items;

[0055]FIG. 3B illustrates a set of trendsetter ratings tables generatedin accordance with one exemplary embodiment of the present invention;

[0056]FIG. 3C illustrates part of a procedure for determining anappropriate size for a set of trendsetters;

[0057]FIG. 3D is a flow chart illustrating the steps performed by anitem popularity/demand prediction engine implemented in accordance withone exemplary embodiment of the present invention;

[0058]FIG. 4 is a time chart illustrating a typical adoption rate of anew item within an online community, identifying particular regionswhere subscribers behave as early adopters, middle adopters and lateadopters.

[0059]FIG. 5A is a illustrates a correlation/relationship betweenvarious items in an online community, such as between certain popularitems, and other mote obscure items;

[0060]FIG. 5B illustrates the basic steps performed by an item trendpredictor identification process implemented in accordance with anotherembodiment of the present invention.

[0061]FIG. 6 illustrates a preferred embodiment of a trendsetteridentification system 600 constructed in accordance with the presentinvention.

DETAILED DESCRIPTION

[0062] The present invention is generally directed, as noted above, tothe identification of persons (or even other non living entities whosebehavior can be studied) that behave or can be characterized as“trendsetters.” In this respect, the term “trendsetter” as used hereinis intended generally to mean those persons who have behavioraltendencies, affinities, or opinions about items which tend to be aheadof their peers—at least from a time perspective.

[0063] Thus, trendsetters are generally persons whose behavior, beliefs,tastes, actions, etc., are imitated and copied by other persons, and/orare simply slightly ahead of the curve so to speak against otherpersons. They act as indicators of the paths that others will take. Insome instances persons will be considered trendsetters by virtue oftheir status within a community, such as the special status afforded tocelebrities. These persons will naturally serve as trendsetters becausetheir behavior, beliefs, biases, taste, actions, etc., are widelypublicized for consumption, and are thus widely imitated by otherperson.

[0064] In other situations, however, persons may behave as trendsetterswithout knowing the role they are fulfilling, and simply because theyhave a form of cultural antenna in tune with the zeitgeist. For example,early adopters of a particular new type of computer can be seen to be aform of trendsetter. Persons who are the first to look for, read and/orspot particular new content (i.e., news stories) can also betrendsetters. Many more examples will be apparent to those skilled inthe art, and as used herein the term is intended to be interpreted inits broadest sense consistent with this disclosure.

[0065] Accordingly, in a preferred embodiment describe below, thebehavior that is being monitored is the adoption of a particular item bya person, group or entity in a time fashion that precedes andanticipates the later actions by peers. Nonetheless it will beunderstood that other aspects of a trendsetter's behavior, beliefs,biases actions, etc., could also be imitated, copied and studied. Forinstance, it could extend to the bidding behavior of an online auctionparticipant, or the particular interface personalizations selected bysome subscribers for their interactions with a website, or the nature ofthe queries they present to a search engine at an online website.

[0066] In other cases, for example, the non-action, rejection ornon-adoption of an item by a trendsetter may serve as a basis forimitation and study for identifying trendsetters, such as in the casewhere a person consistently rejects a particular item in a head to headcomparison against other items. The present invention, therefore, canalso be used to calculate rejection prevalence or a rejection rate of anitem by a group of trendsetters.

[0067] As can be seen from the present disclosure the present inventionis primarily concerned with “useful” trendsetters, meaning those personswhose adoptions end up becoming sufficiently popular or imitated withina large enough community. The degree of popularity, and the size of thecommunity can be extremely variable of course, but the point is toexclude “early” adopters who have impulsive, indiscriminate behaviors(i.e., buy anything new). Such persons do not communicate usefulinformation in the sense that their behavior is not sufficientlypredictive of a future trend.

[0068] Conversely, persons whose behavior tend to behind the generalpopulation, or can be considered as late adopters of an item, can begenerally described as “trend laggards.” As explained below, identifyingand monitoring trend laggards can also be useful in some contexts. Thuswhen the term “trendsetter” is used below, it will be understood that itcould also refer to a trend laggard as well, except where it is apparentto one skilled in the art from the context that such is not logicaland/or consistent with the present description.

[0069] Further as used herein, the term “item” is also intended in itsbroadest sense, and my refer, for example, to a product (books, auctionarticles, music recordings, and the like) a service, a human readablecontent piece (an online news story, video, comment, a web page, awebsite, an interface customization, etc. The item could even refer to amore abstract concept, such as a person, a security, an opinion, abelief, etc. Basically, the term can refer to anything which can beaccurately measured in connection with a group of individuals orentities, including persons within an online community, websitesassociated with particular subject matter, etc.

[0070] It should be noted that the trendsetters identified by thepresent invention may or not be drawn from the community underconsideration. In other words, it is entirely possible that theexistence and behavior of trendsetters within one community can be usedas a useful gauge for determining the expected demand for an item in anunrelated community. For example, the consumption of ads by a particularset of persons within a particular electronic community might be asufficiently useful proxy for predicting the behavior of a different setof persons expected to view such ads in a different medium (i.e.,television.) The predictions of a stock price by one or moretrendsetters may be used to anticipate the performance of a stock withina trading market.

[0071] Furthermore, as used herein, a trendsetter could refer to asingle person, or to a group of persons having some commoncharacteristic, such as membership in a group, or a particulardemographic profile. Trendsetters can also be broken out andcharacterized by sub-group, and demographic group as may be desired orconvenient. For instance, trendsetters may be further classifiedaccording to sex, age, or income. In another application, they may beclassified according to subgroup.

[0072] Thus, even within a single community, one group may have one setof trendsetters for a group of items, while in another group a differentset of trendsetters may be identified for such items. This allows forfiner differentiation at a level that is more personal. An example ofthis are the subgroups and communities created by Amazon from itscustomer base, such as groups of customers from a particular domain,customers from a particular zip code, phone area code, etc. Otherexamples will be apparent to those skilled in the art.

[0073] In some instances, a non-human entity could be used as well, ifsuch entity's behavior can be meaningfully compared against otherentities. As an example, the invention could be used to determine whichcompanies are leaders in using certain types of terminology in pressreleases, product descriptions, etc. Even web pages or websites can beexamined for trendsetter status in some cases.

[0074] Finally, even items themselves can be characterized as forms oftrendsetters for reasons set out further below, if they provide usefulstatistical predictive value on other items. Other examples will beapparent to those skilled in the art, and thus it should be appreciatedthat the invention is not limited in this respect Finally, whiletrendsetters in the preferred embodiment are identified by way of theiradoptions of items, this is not the only mechanism that can be used. Forexample, a trendsetter may be determined with reference to otherindicia, such as implicit and explicit inputs. In other words, it is notonly adoptions that may signify a trend setter.

[0075] The reasons why trendsetters are important are many, and includegenerally the following:

[0076] (1) Members of an online community generally like to beidentified and appreciated for their contributions. The inventionprovides a positive label for their activities and increases thelikelihood that they will share personal information that can be used bya website operator;

[0077] (2) Other members of the online community like to be keptinformed of new trends (i.e., trendy items) and who is associated withsuch trends;

[0078] (3) Larger collections of members (i.e. such as message boardsdevoted to a topic, online groups associated with particular topics,etc.) can also be analyzed and classified as trend setters within alarger subscriber population. For example, a number of Yahoo! MessageBoards, and/or Yahoo! groups could be studied to determine which of suchboards or groups is a trendsetter on a particular topic. These boardsand groups can then be identified online for the benefit of othermembers, so that they can determine where to go for learning new trends.

[0079] (4) Members of the online community can voluntarily “subscribe”to a trendsetter (person or group), and thus gain the benefit of thelatter's early prescience concerning the popularity of items;

[0080] (5) By measuring the acceptance or adoption prevalence, oradoption rate of an item by a set of trendsetters, a supplier of theitems can better gauge expected demand or potential for the item;

[0081] (6) The degree of adoption by trendsetters can be measured andused to influence a recommender system. It is well known, for example,that collaborative filtering systems suffer from “first rater” problems,and thus the present invention can be used to influence and bias arecommender system by disproportionately weighting the selections ofcertain individuals at an early stage to accelerate the learning of theCF system;

[0082] (7) The profiles, demographics, etc. of trendsetters can begleaned by outside entities and used for advertising/marketing purposes,in the same manner as used by the aforementioned Yahoo! solutionsprogram;

[0083] (8) Since trendsetters are some of the most valuable assets of anonline community, identifying them early allows a website operator toprovide them with inducements and rewards to stay within the onlinecommunity;

[0084] (9) Product marketing/sales statistics can be determined fromstudying the trendsetters, including an overall trendsetter adoptionpercentage, adoption prevalence, adoption rate, as well as benchmarkcomparisons to prior popular items;

[0085] (10) Trendsetters can also be used for influencing the score of asearch engine. It is well-known that some search engines use a form ofrelevance scoring in presenting search results. By weighting itemsassociated with trendsetters (which can be items adopted by persons, orindividual sites that are rated as trendsetters among other websites)more highly, this can further serve to improve the performance of suchsystems.

[0086] (11) Other preferences of trendsetters can be explored andpresented for public viewing, such as personalization features andfunctions they may use at content provider sites, including contentcategories they review, websites they visit, and interfacecustomizations that they use.

[0087] These are but a few reasons why identifying trendsetters are anextremely useful process, and others within the scope of the presentinvention will become apparent to those skilled in the art from thepresent disclosure.

[0088]FIG. 1 is a flow chart illustrating the steps performed by atrendsetter evaluation and feedback process 100 implemented inaccordance with one exemplary embodiment of the present invention. Asdescribed herein, such process (as well as the other processes explainedbelow) can be embodied and expressed in a variety of software programs,routines, etc., that run on one more client or server devices coupled tothe Internet, using techniques that are well known in the art. The typesof systems which can embody the present inventions can include a varietyof conventional hardware platforms known in the art, including dataprocessing equipment and computers with a wide range ofcomputing/storage resources and capabilities. Accordingly, the detailsof such software and hardware implementations are not material except asdiscussed herein with reference to specific aspects of the invention,and they will vary significantly from application to application basedon a desired performance.

[0089] As noted in FIG. 1, a first step 110 is to identify thetrendsetters, which, as noted above, preferably will be from within aparticular online community, but need not be. For example, an onlinecommunity might consist of all subscribers to Amazon, EBay; Netflix,etc., or those users who frequent Yahoo!, Google, etc. Alternatively,the trendsetters could be determined by reference to a sub-group, if theoverall online community population is not easily manageable, and/or tomake the trendsetter identifications more relevant to particularcategories of users. A preferred process of identifying the trendsettersis explained in more detail below, using a variety of electronic datacollection techniques.

[0090] At step 120 the adoption prevalence (and/or adoption rate) forone or more items is measured for the trendsetters. Generally speaking,these particular items represent newly introduced items to the onlinecommunity, so that they are not already adopted by a large percentage ofthe online community members. Again, a preferred process of identifyingthe adoption prevalence (and/or rate) is also explained in more detailbelow.

[0091] During step 130, after determining the adoption prevalence, avariety of different reports, feedbacks, responses, etc., can begenerated based on a value of the measured adoption prevalence. Thisincludes, for example, the options identified at 135, and which werealluded to earlier.

[0092] For instance, a website operator could generate a list of“trendy” items based on an identification of new items which haveachieved a particular adoption prevalence (or adoption rate) by thetrendsetters. The trendsetters themselves could also be identified,typically by their online handles. The aforementioned options include,of course, publishing such data for online consumption by other membersof the community, in a manner similar to that done by buzz.yahoo.com.The percentage of trendsetters who adopt over time, as well ascomparisons to adoption rates for other items could also be published.

[0093] Similarly, a website operator could use the trendsetter data toprovide specialized custom reports for particular entities who may wishto see the acceptance rate of a particular new product/service. Theentity may be a music publisher, for example, who desires to know theacceptance rate of a particular title. In such case, the music publishermay be able to generate an expected demand prediction for the item bythe remainder of the online community, well in advance of the actualdemand. This tan assist in accurate and efficient planning for productadvertising, manufacturing, shipping, administration, etc.

[0094] Alternatively, the invention could be used in a manner similar tothat described by Mallon et al., except that the “buzz” measurementcould be made only of the identified trendsetters, instead of the randomcategories envisioned by the Mallon et al. disclosure. Thus, thepredicted demand for movies, music, and other entertainment could bepredicted by reference to a more reliable data set. Advertisers can alsouse the present invention to measure the effects of advertising onparticular groups, particularly trendsetters.

[0095] The website operator could also provide a mechanism for the otheronline community members to “subscribe” to particular trendsetters, muchin the same way as that done at the launch.yahoo.com website. The latterwebsite allows an individual user to be “influenced” by other members,so that the tastes of such members are imposed in the form of musicalselections for the user. The limitation of this site, however, is thatit does not identify those members who may be trendsetters, sosubscribers are not able to glean the status of another member merely bylooking at the data for such member. Moreover the Launch site allows aperson to be “influenced” by an entire community, as set out in WorldApplication serial no. 02/05140 to Boulter et al (U.S. Ser. No.09/79,234) incorporated by reference herein. These are useful features,but they do not allow for specific tailoring of musical tastes. Usingthe present invention, however, an online member can elect to be“influenced” or kept informed of a particular trendsetter's (or a groupof trendsetters) selection of items (be they music, products, services,or something else). This feature has the advantage as well of allowingan e-commerce site to achieve more rapid and effective penetration ofnew items to a community, and before its members potentially hear ofsuch articles at a different site. Again, from the perspective of ane-commerce vendor, it is preferable if they are the first to present newitems to persons who frequent their sites, because they run the risk oflosing a subscriber or even a potential sale if such person learns of anew item elsewhere.

[0096] A similar benefit can be used in connection with a recommendersystem. Again, recommender systems are well-known and commonly used ate-commerce sites. These systems are known, also, however, to suffer fromso-called “first-rater” problems, and this leads to the problem thatthey do not react very quickly to the introduction of new items or tochanges in attitudes by their users. By exploiting the early scoutingintelligence provided by trendsetters, e-commerce entities canessentially “tune” their recommender systems (typically based on acollaborative filtering algorithm) very early to substantially reducethis type of problem. In other words, an e-commerce recommender systemcan be programmed in one implementation to weight the adoptions oftrendsetters more heavily than other users, and thus essentiallyaccelerate the learning process for new products. In a collaborativefiltering system, the trendsetters could be artificially multiplied and“planted” into different user clusters to influence the recommendersystem behavior. For an example of the use of “clustering” incollaborative filtering mechanisms in which the present invention couldbe used, see the recent article by Wee Sun Lee entitled “CollaborativeLearning for Recommender Systems” appearing in the Proc. 18thInternational Conf. on Machine Learning (2001) and which is alsoincorporated by reference herein.

[0097] Other techniques for incorporating the teachings and behavior oftrendsetters, and mechanisms for influencing the operation of arecommender system will be apparent to those skilled in the art.

[0098] Other useful information can also be gleaned from the trendsetterdata, including their respective profiles, demographics, other relatedtastes and dislikes, etc. This information is extremely valuable from anadvertising and marketing perspective, since many entities would like tointeract and solicit feedback from such types of individuals. If ane-commerce site can effectively identify such individuals, this databasecan be marketed as a valuable commodity to other entities.

[0099] For similar reasons, since trendsetters are valuable assets foran online community, identifying them early allows a website operator toprovide them with inducements and rewards to stay within the onlinecommunity. Furthermore, the present invention can be used to “mine”other online communities for the purpose of locating, verifying andcontacting other potential trendsetters for particular items, orcategories of items. For example, one or more websites may agree toallow limited inspection of their respective subscriber databases toother websites for the purpose of exchanging useful marketinginformation. This function, again, can be valuable for increasing thestickiness and appeal of a particular website.

[0100] Identifying Trendsetters

[0101]FIG. 2A is a flow chart illustrating the steps performed by atrendsetter identification process implemented in accordance with oneexemplary embodiment of the present invention. As seen there, a firststep 210 examines which items are the most popular within the communityat a given time, which may be the present, or some prior date. It shouldbe apparent that the process can be executed to identify trendsettersfor a single items, multiple items, or items within a larger logicalgrouping, such as a category or sub-category of items. For example, anitem might be a particular title of a book; a category of books might belogically grouped by artist, genre, publisher, etc.

[0102] It should be clear that “popularity” of an item (or items) couldbe measured by reference to numbers of units sold, a number of unitsrented, a number of page views, a number of queries, a number ofmessages, etc., and the degree by which an item is deemed to be popularcan be measure in any number of ways, including, for example, apercentage. Thus, in the present example, an item is deemed “popular”when it is among the top 10, or among the top 10% of items. Otherapplications are likely to use other benchmarks for determiningpopularity.

[0103] In any event, after identifying the set of popular items, theprocess then calculates a number Y of persons at step 220 that it isgoing to use and characterize as “early” adopters, or trendsetters. Thisvalue, of course, could be changed on an item by item, or category bycategory basis as needed. The trendsetter number could be generated as aconstant (i.e., the first 100 people), or as a percentage of the totalwho have adopted the item. Furthermore, trendsetters could becharacterized on a graduated scale. In the latter case, for example, thefirst 100 adopters may be given one weight, the second 100 adopters alower weight, etc., so that multiple levels of trendsetters could beestablished for an item.

[0104] In another instance the value of Y can be gleaned by statisticalanalysis/prediction. In other words, by studying an adoption prevalence(or adoption rate) for popular items, one skilled in the art candetermine experimentally, using varying confidence levels, what thesmallest value of Y is required to serve a useful prediction value. Thiscalculation has utility because it is preferable, of course, to reducethe universe of trendsetters to its minimal but still useful value. Insome cases the invention can calculate both types of values for thetrendsetters: i.e., one calculation for identifying the number Y oftrendsetters, and another value Y′ for identifying the smallest numberof trendsetters that can yield useful predictive information.

[0105] Again the specific calculations will vary from application toapplication, and will be unique to each environment and to theparticular needs/interests of an e-commerce site.

[0106] At step 230, the process then identifies the actual trendsettersby examining the first Y adoption times of each item in the set ofpopular items. Again the trendsetters are preferably identified fromwithin an electronic community using a conventional electronic datacollection technique, but do not have to be. This is because in somecases, for example, the nature of people's behavior may be such that afirst group's individual and collective behavior can be more accuratelymodeled, tracked, and used for predictive value for a second unrelatedgroup. The latter, for example, may not provide sufficient trackinginformation that can be meaningfully analyzed.

[0107] Finally, at step 240 the trendsetters are explicitly listed byitem, by a group of items, or in aggregate across an entire samplingpopulation. These lists can be used as noted below for private use inmarketing, planning, and/or they can be published electronically onlineas well for community consumption. In the latter case a particularcommunity can see who the trendsetters are for a particular item, or whothe trendsetters are for a category of items, or who are the overalltrendsetters across all items.

[0108] A preferred process for identifying trendsetters is depicted inFIG. 2B with reference to a first trendsetter matrix which correspondsgenerally to a database of records identifying, in the far left handcolumn, a particular person, and in the adjacent columns the identity ofparticular items that are available in the database. Each intersectionof row and column identifies whether such person adopted (i.e., lookedat, purchase, rented, queried, talked about during an electronic datacollection session) such item, and, if so, what score they achievedvis-à-vis a trendsetter rating. For example, for person A, he/she hasachieved a trendsetter score of 5 for item #1, a score of 4 for item #2,etc. The items are further logically grouped into categories as noted,so that items #1-#3 belong in a first category, while item #4-#9 are ina second category.

[0109] The trendsetter matrix is compiled from ongoing loggings of userselections of items, and because of its nature does not have to beperformed in real-time. In fact, it may be calculated daily, weekly, oreven on a periodic basis for a target set of items as requested by aparticular third party to generate customized reports. An example of theusage of such types of matrices, in a related context of examining userratings of items for a collaborative filtering algorithm, is discussedin an article by Melville et al. entitled “Content Boosted CollaborativeFiltering” from the Proceedings of the SIGIR-2001 Workshop onRecommender Systems (New Orleans, LA September 2001) and which isincorporated by reference herein.

[0110] In one embodiment the correlation matrix can include all of theitems in an item database, so that as new items are added, someadditional predictions can be made about them as explained below. Insituations where additional demand type predictions are not needed ordesired, the correlation matrix may be composed only of “popular” itemsas determined from the above.

[0111] It is understood of course, that this depiction is asimplification of only of a small section of a person-productcorrelation matrix which is intended to help in comprehending thepresent invention. In any actual commercial application, the form of thematrix, the type of data and the size of the same could be significantlydifferent. Nonetheless, even from this simplified depiction, one skilledin the art can appreciate how one or more trendsetters can be identifiedfrom the aforementioned matrix.

[0112] Accordingly, in FIG. 2C, a table of trendsetter scores iscompiled from the trendsetter matrix. The trendsetter scores can bederived for individual items, groups (categories) of items, or even forthe entire item set.

[0113] Thus, for example, for item #1, Persons A, C and F could beclassified as trendsetters, if a threshold value of 3 is specified forthe trendsetter score. Again, the rating required to be identified as atrendsetter could vary from community to community, and it is notnecessary to use a scale of 1-5; any scale, in fact, which allows forranking is entirely suitable.

[0114] The trendsetter scores are first tallied across all items withina category, and then normalized by the number of items adopted by theperson within the category. Some scores and ratings may be adjustedstatistically for the following reasons.

[0115] First, if desired, users who have adopted over a certainthreshold percentage of items may be eliminated statistically to avoidbiasing the results. That is, some persons may be simply indiscriminate(albeit also early) adopters, and thus users of the invention mighttrack and eliminate such types of users. Again, the invention can beused to identify users who simply purchase a lot of items as randomconsumers of everything, not trendsetters per se; the choice of course,can be determined on a community by community basis.

[0116] Similarly, persons who have not adopted a sufficient number ofitems within a category may also be eliminated, to avoid attributingtrendsetter status to persons with insufficient track records. Thus, theinvention can be used to glean the user's overall behavior andtrendsetter rating within a category of items, by examining theirbehavior over a large enough sample set to reduce random errors.

[0117] In accordance with this above, therefore, it can be seen thatwithin category 1, users A and F can be classified as trendsetters usingone set of criteria. Persons C and E simply have an overall score thatis too low, even as they have adopted a sufficient number of items (2)in this instance. A dash (-) is used to denote that the person has notadopted such item. Even though user D has a reasonably high raw score(5) within category 1, he/she is not characterized as a trendsetter,because their normalized score is (5/3)—i.e., their raw score/#itemsrated. Thus, D's purchase of item #3, in which they scored no points, isindicative of their late tendency in some cases, so they are not ratedoverall high enough to merit trendsetter status. In this manner, theinvention further rewards accuracy in the behavior of users indiscriminating their item adoptions. Person G has not adopted asufficient enough number of items to be rated fairly, so they do notqualify in this instance for trendsetter status.

[0118] Similarly, in the Category 2 items, persons B, D and G nowqualify as trendsetters, based on the same kind of scoring logic asnoted above. From the above it can be seen that persons who aretrendsetters over one set of items (i.e. person A is a trendsetter inCategory 1) may not be trendsetters with respect to a different set ofitems (i.e., Category 1).

[0119] An overall score can also be calculated, as shown in the righthand columns of FIG. 2C. In this instance, users B, F and G are excludedbecause they have not sampled (or adopted) a sufficient number of items.The highest three scores belong to D, C and A respectively, so they maybe identified as overall trend leaders. It can be seen, therefore, thateven though C is not a trendsetter at either category level, he/shecould still be eligible for overall trendsetter status based on theirtotal aggregate behavior.

[0120] Again, the thresholds for scores and items ratings can be variedfrom the above, and are expected to be adjusted differently from case tocase within the scope of the present invention. If desired, differentratings criteria could be used to identify a trendsetter at the itemlevel as opposed to the category level or aggregate level. For example,at the aggregate level, a score greater than 2 may only be required toachieve a trendsetter status. By mining and exploring the data set inthis fashion, a large number of interesting and useful trendsetterparameters can be gleaned for a particular population sample.

[0121] Trendsetter Analysis to Determine Trend Predictors

[0122] Another useful tool for identifying and classifying trendsettersin aggregate across a community is illustrated in FIGS. 3A to 3D. Thissecond embodiment of a trendsetter identification process can be usedalone and/or in conjunction with the process described above for thereasons set forth below.

[0123] As seen in FIG. 3A, for each popular item (Xi) in the set of Npopular items (X1, X2 . . . Xi . . . XN) a determination is made of thefirst M adopters (Y[xi]1 to Y[xi]M). Again, the choice of N and M aresomewhat arbitrary, and are expected to vary from application toapplication.

[0124] A first trendsetter listing table is then created as shown inFIG. 3B. Each item Xi is processed until a table is derived of theentire set of adopters (Y1 to Yp identified in a first column) whoqualified as a trendsetter for one more items, along with theiraggregate trendsetter scores (in the second column). Since a particularuser may be an early adopter of more than one item, his/her score isincreased within the list for every such instance. Thus, for example, ifa person Y[x1,1] is an early adopter (meaning anywhere within the top Mpersons) of ten items of the top N items, then they would have anoverall trendsetter prediction rating (Σ) of 10 in the second column ofthe table of FIG. 3B.

[0125] The trendsetter ratings can also be normalized, again, withreference to the total items adopted by the trendsetter underconsideration. Thus, as shown in FIG. 3B, the third column in thetrendsetter listing table indicates a calculation to denote a normalizedtrendsetter score (NΣ).

[0126] As an alternative the raw trendsetter scores for a particularitem could be scaled in accordance with the degree of “earliness,” sothat a person could receive a score that is not simply a constant. Forinstance, if M is 500, a person may receive a score of 10 for being inthe top 100 adopters, and a score of only 5 for being between the top100 and top 500. The person may in fact receive a score equal to his/heractual adoption number within the population. Similar examples will beapparent to those skilled in the art.

[0127] Again, as noted earlier, early adopters who have rated too manyitems, or an insufficient number of items, may be excluded if desiredfrom the tabulation process to arrive at the trendsetter listing table.

[0128] In any event, as further shown in FIG. 3B, the set of aggregatetrendsetter ratings are then processed from the listing table togenerate two ordered trendsetter ranking tables, one by raw score, andone by normalized score. Therefore, as seen in FIG. 3B, TrendsetterRanking Table #1 is ordered in accordance with those persons who haveachieved a highest overall trendsetter score. Conversely, TrendsetterRanking Table #2 is ordered in accordance with those persons who haveachieved a highest overall normalized trendsetter score.

[0129] These two sets of aggregate rankings can be used: for a varietyof purposes. As a first example, it may be extremely valuable, from amarketing, planning, sales and/or advertising perspective to know whichand how many members of a group act as benchmarks and early barometersof popular items. By understanding such groups, an e-commerce entity canbegin to make predictions about items that have not yet achieved, butwhich may eventually achieve great success (prevalence) within aparticular online community.

[0130] A useful benchmark that can be derived for any community isdetermining the various confidence levels to predict that an item islikely to achieve great success, based on the number of trendsetters whohave actually adopted an item. In other words, another calculation thatcan be performed in the present invention is a determination of how manyof the top trendsetters are needed in order to make predictions aboutthe expected popularity of an item, and correspondingly, how accuratesuch prediction is likely to be.

[0131] The determination of the number of top trendsetters that areneeded to generate useful predictions (i.e., so called trend predictors)can be determined experimentally using known techniques.

[0132] One basic approach would be to simply take the top K trendsettersusing a cutoff that is based on a balance of expediency, accuracy, andperformance. The top K trendsetters are then used as proxies andbenchmarks below for gleaning the expected behavior of an item, or agroup of items, which are not yet popular, but which have been selectedby some sub sample of such top K trendsetters.

[0133] Another approach for determining K is shown in FIG. 3C, where theactual adoptions of items X1 to XN are listed for the K highest rankingmembers taken from one of the trendsetter ranking tables. K may bedetermined, therefore, by examining how many members must be listedbefore all of items in the set (X1 . . . XN) appear in at least one ormore of the individual trendsetter adoptions. Alternatively, K may beselected by examining how many members must be listed before the top 10(or 20 or 50, etc.) items appear in each of the individual trendsetteradoptions. This latter approach helps to create a very focused andprecise set of trends predictors. Yet another approach would be to varyK statistically by examining what benefits (i.e., such as reduction inerror—or improvement in prediction) are provided through the incrementaladdition of another trendsetter as a trend predictor.

[0134] Trend Prediction

[0135] Nonetheless, the invention is not limited in this respect to anyparticular selection scheme, and regardless of how K is calculated,preferably a sub sample of the trendsetters are then identified in someform as trend predictors. Again, the trend predictors might be takenfrom one or both of the Trendsetter Ranking tables in FIG. 3B(normalized or unnormalized), and the final choice may be determinedexperimentally by examining which subsets tend to give the best results.The trend predictors in the population are then used for generatingvarious forms of reports and predictions for marketing/sales/trendanalysis in the following manner.

[0136] For instance, a supplier of an item may wish to know what theanticipated adoptions (sales, rentals, views) will be for an item withinthe online community for planning purposes. By measuring the adoptionprevalence of the product among the trendsetters, and more particularly,by the trend predictors, the supplier can determine the likelihood ofsuccess of such item, based on the fact that such proxies tend to adoptitems very early that later turn out to be very popular. The measurementand prediction for a first item might also be used to triggerintroduction of a second related item, if the adoption prevalenceappears sufficiently large so as to suggest that the two items will bepopular within a particular online community.

[0137] The adoption prevalence for an item can be measured in a numberof ways. For example, the raw number of instances which such item hasbeen adopted by the trend predictors could be measured. Alternatively, apercentage figure could be determined, as well, to indicate a relativepercentage of trend predictors (or trendsetters) who have adopted theitem.

[0138] For example, in the case of the person-item matrix of FIG. 2B, ifitem #3 and #9 are new items, their adoption prevalence by thetrendsetters can be calculated as follows: for item #3, the adoptionprevalence is 50% (since only A has adopted the item, and F has not)while for item #9, the adoption prevalence is 33% (since only D hasadopted the item, and B and G have not). This is of course, asimplification, and those skilled in the art will appreciate that actualdata sets will be significantly larger, and that other mechanisms couldbe used to compute such adoption prevalences.

[0139] In another variation of the invention, a rejection of an item, tothe extent it can be accurately determined, can also be specified aspart of the person-item matrix, in the form of a negative number, and invarying degrees. For example, if a user is shown an ad for a particularitem, and does not respond positively to such ad in any fashion (i.e.,through queries, content viewings, etc.) then the item could be given anegative rating, signifying that it was rejected by that user. If the ador other offer for the item is rejected again in the future, thenegative rating could be increased, up to a maximum limit signifying a(perceived) unconditional rejection.

[0140] The benefit of collecting data on rejected items, of course, isthat the attitude and behavior of the trendsetters and/or trendpredictors towards such items can also serve as valuable marketing andprediction information. The negative ratings, of course, would beignored during calculations of the trendsetters and trend predictors.Nonetheless, it can be seen quite clearly that the trendsetters can helpidentify early on both products that are likely to be popular, as wellas items that are not likely to be popular.

[0141] The adoption prevalence could also be studied over time, to gleanother useful trend predictive data, such as an adoption rate. Thus, thetrend predictor penetration rate could be examined on a day to day, weekto week or other specified time basis to see changes in such rate overtime. Again, comparisons could be made to historical data as well forbetter analyzing the behavior of popular items, and predicting thebehavior of a new item. An e-commerce vendor may determine, for example,that only certain rates of adoption by the trend predictors exceeding athreshold are meaningful predictors of the popularity of an item.

[0142] For example, in a very simple use of the trend predictors, theycan be compiled into a list, and identified to advertisers/marketresearchers. These entities, in turn, can then target their advertising,surveys, etc. to such trend predictors very accurately to glean valuableinsights that would otherwise remain buried on a mountain of aggregatedata. For instance, as noted earlier, an identification of the topicsand interests of the trend predictors (and/or trendsetters) could bemeasured using techniques such as described in Mallon et al.

[0143] The trend predictors in some instances can serve as facilitatorsfor introducing new popular material into a community, because they tendto lead the remainder of the community. By presenting such new itemsdirectly to the trend predictors, the likelihood of success of such itemalso concomitantly increases.

[0144] Finally, in some cases it may be desirable to study the otheradoptions of items made by a group of trendsetters (or trendpredictors), to see to what extent they also share certain itemselection adoptions that are substantially different from the overallpopulation being studied. For example, certain obscure content titles(books, movies, articles) may be viewed with significantly greaterfrequency by trendsetters as compared to other members of the community.These additional items (or groups of items) can serve as additionalforms of fingerprinting and identifying trendsetters and trendpredictors in the future at an early stage, even when information may beincomplete for a particular individual.

[0145] The overall process 300 for generating item adoption rates andpredictions is depicted generally in FIG. 3D. As noted there, a list ofnew items or items specific to a particular supplier are used at step310. The adoption prevalence within the trend predictors (or thetrendsetters as may be desired) is then measured at step 320. At theend, a report can be made at step 330, to identify trend data for theitems. Again, a vendor or other supplier of an item can thus measure, atany moment in time, the behavior and performance of a particular itemwithin a very specific but important segment of the population of theonline community.

[0146] The benefit of the present invention is also evident as it allowsfor rapid identification of trendsetters and trend predictors, even fromrelatively new additions to the community of members. That is, unliketraditional recommender systems which require extensive amounts of datacollection, the behavior and classification of a member as a trendsettercan occur fairly early and quickly based on an adjustable number of itemadoptions. This makes it possible for new ideas and tastes to be morerapidly integrated and disseminated within a particular community,enriching the experience of other members as well. Furthermore, thepresent invention helps to minimize the effects of “popularity bias,”which is known to cause recommender systems to frequently recommend onlyitems which are already popular throughout the entire community. This isbecause, as can be seen herein, the influence of certain persons, suchas trendsetters, can be weighed at an early stage of an item's adoptionto improve its visibility to other members.

[0147] In some instances, for example, a content service provider maysimply use the trendsetters or trend predictors for providingrecommendations for items, in lieu or as a supplement to a traditionalrecommender system. A “content service provider” (or service provider)in this instance refers generally to an entity that is not directlyinvolved in the creation of new content, but, rather, merely distributesit in some fashion as a service to subscribers.

[0148] As alluded to earlier above, in some cases an e-commerce websiteoperator serving an online community may benefit from identifyingtrendsetters, trend predictors and trend predictions from off-linecommunities, or even other online communities. This type of process canbe automated, as well, as set forth in U.S. Pat. No. 6,571,234(incorporated by reference herein) based on operator selections torapidly and automatically inject new materials for consumption by anonline community.

[0149] Furthermore, as noted above, it is possible to examine andidentify smaller “group” or community trendsetters within larger onlinesubscriber lists managed by such entities as Yahoo!, Amazon, EBay, AOL,MSFT, etc., In other words, a content service provider may want to alertand publish lists of particular groups that are trendsetters onparticular topics. Thus, for example, an e-commerce entity such asYahoo! could use the present invention to analyze which message boardsor groups were the first to discuss certain types of products, brands,services, etc. These trendsetter groups can be identified, again, forgeneral interest or marketing purposes, on a topic by topic, group bygroup basis.

[0150] In some instances it may be desirable for a first websiteoperator to induce trendsetters, trend predictors, etc., to join aparticular community. This can be done by free subscriptions, freeservices, free products, financial awards, or other similar incentives.By identifying such persons in other online communities and successfullypersuading them to contribute to a particular community (even if onlyindirectly, such as through a recommender system) a website operator canthus boost and improve the overall attractiveness of an online communitysite.

[0151] For other applications it may be possible to imitate the behaviorof trendsetters and trend predictors who exist in another online domain.For example, an online community might create a set of proxies who mimicthe behavior of another group of persons, in order to obtain the benefitof the input of the latter. The profiles of the proxies could besynchronized on a regular basis to make certain that they reflectcurrent trends.

[0152] Moreover, in some cases, it may be desirable to see how an actualtrendsetter (and/or trend predictor) from within the community (or evena proxy trendsetter based on a trendsetter from another onlinecommunity) is treated by various online content providers, again, forthe purpose of collecting marketing intelligence. Thus, a first onlinee-commerce site may create a proxy that imitates a trendsetter fromanother online community, and then test their own site (i.e., throughjournaling page views presented to the proxy account, tabulatingrecommendations made by a recommender system, etc.) with such proxy tosee how their site (or other sites) presents itself to such proxy. Thistechnique can be used, for example, to determine if advertising isreaching the appropriate audience.

[0153] In another variation of the invention, the trend predictors couldbe selectors for a particular stock or publicly traded equity. Thus, ina stock picking community, the invention could be used to identifyoverall successful “early” adopters of successful buy and hold equitiesfor the benefit of other members. For example, some persons maydemonstrate that they have a higher degree of prescience in selectingstocks just before they rise substantially (or even declinesubstantially) in price. When such trend predictors select new stocks insufficient numbers (i.e., as measured by a prevalence rate) this datacould be communicated to the other members to alert them to the newestpotential hot pick.

[0154] In still another variation of the invention, the trend predictorscould be used by an online search engine, such as the type of systemused and operated by Google. The latter uses a form of weighting whenpresenting webpage results to queries, based on a number of links tosuch webpages. In many respects, the lack of links can be analogized toa lack of ratings in a recommender system; without enough persons beingaware of a website, it cannot be linked, regardless of how relevant itmay be.

[0155] The present invention can address such deficiency in a searchcontext as well, by allowing certain websites, which are likely to belinked to later by a large number of entities because they are trendsetters, to be used before such time to render more relevant results.Thus, using the processes noted above, data mining could be performed onentire websites, not just individuals, to determine correspondingwebsite and/or web page trendsetters. In some instances, for example,historical data on the composition and content of websites can begleaned from online databases, such as the Wayback Machine that isavailable at archive.org. In other cases a search engine company ortrend rater for websites can directly collect content on a regular basisfrom selected websites in order to rate their trendsetter capabilities.Again, these websites could be identified by topic to search requestersas well as part of a search on a particular search term, so that thelatter are made aware of which websites tend to lead the overallInternet in terms of early adoption of material, and thus are likely tohave the most “current” information now on subjects, even if they arenot the most highly linked to. Thus, in examining hits, the age of apage could be considered as well. The websites could be classified intocategories for ease of reference and comprehension.

[0156] Thereafter, in response to a particular search query, a searchengine could consider the trendsetter rating of a website as part of aweighting algorithm, and the age of a page to present results based inpart on the trendsetter status of such website. This additionalparameter, therefore, could be used for weighting results, andpresenting either a single trendsetter adjusted “hit” list or anadditional trendsetter-based results list to supplement a normal searchquery. The existence and extent of website trendsetters could also betabulated, compiled and presented for public consumption at searchengine websites.

[0157] Finally, as noted earlier, the adoption prevalence of certainitems (which could be keywords or phrases) can also be studied across acollection of websites to identify the potential for new trends, or thedemand for certain items.

[0158] In an electronic auction application, such as that maintained byEBay and similar companies, trendsetters are persons who havedemonstrated that they can anticipate the expected demand for new typesof collectibles that then turn out to be valuable and/or highly indemand. By posting the new activities of such trendsetters (in somecases anonymously, or in aggregate broken down for different types ofitems) other users can determine what is likely to be a “hot”collectible item in the future, and thus participate at an early stagein the adoption of such items before it becomes too popular, or risestoo much in value. The invention is not limited to auctions, of course,and it can be seen that it can easily be extended to other purchasingenvironments where it is useful to see the behavior of trendsetterbuyers/sellers. As noted earlier above, moreover, a prediction can begenerated for an auction item, based on demand exhibited by trendsetters for such auction item, to determine its potential popularity,and/or to set an initial asking price, to set a reserve price, etc.

[0159] Finally, because of the inherent value associated withunderstanding early adopter behavior, an e-commerce site may charge asubscription fee, or an additional fee, for the privilege of observingsuch activity. Again, the above are merely examples, and a myriad ofother embodiments of the invention will be apparent to those skilled inthe art, across a variety of environments which benefit from theidentification and use of trendsetters.

[0160] Use of Trendsetters for Other Purposes

[0161] As alluded to above, in another variant of the invention, thetrendsetters can be defined within an electronic community, and yetserve as predictors for events outside of such community—i.e., beyondjust the prediction of the likely demand for an item within thecommunity. These events could be associated with sales of products(books, movies, automobiles, recreational equipment, pharmaceuticals,food, content, etc.) or some other article/service. Thus, at step 310 ofFIG. 3D the list of popular items may not even be items that are madeavailable to the online community by an e-commerce website operator,but, rather some other item outside the realm of the online community.

[0162] For example, in the Mallon et al. application, it is noted thatan overall “buzz” for a movie is measured within an online community,and this buzz is used to predict the potential commercial success ofsuch movie in a release to the general public. In a similar fashion, thepresent invention could be used to measure this same overall “buzz,” butwithin a more defined, focused and meaningful population sample—namely,identified trendsetters within an online community.

[0163] To do this in a movie prediction application, for example, thetop 100 current movies (in gross receipts or attendance, or some othermeasure) is specified at step 310. Then, by performing a similaranalysis to that noted earlier, a community website operator coulddetermine the first “adopters' of such movie within an online community.This could be done, for example, by examining the dates/times whenmembers first “adopted” the movie, such as by reading an ad about themovie, discussing the movie, or reading an article about the movie.Other techniques for measuring an “adoption” will be apparent to thoseskilled in the art.

[0164] Thereafter the identified trendsetters and trend predictors couldbe used to predict the popularity of a new movie. The movie could be“introduced” into the online community in the form of one or more adspresented electronically, one or more stories, one or more excerpts, oneor more dedicated newsgroups, etc. By measuring the prevalence ofadoptions made by trend predictors, the present invention can thus mimicand yet provide a superior prediction to that described in Mallon et al.

[0165] The above is just an example, of course, and other techniques andvariants will be useful of course for predicting prospective economicactivity for other types of products, services, etc. The invention canclearly be extended to other types of predictions for demands for otherproducts and services.

[0166] New items can be introduced to an online community (or otherpopulation) through a variety of means, including online advertising,and their adoption prevalence then measured among trendsetters and trendpredictors. Furthermore, by comparing the changes in adoptionprevalence, an advertising entity can measure an effectiveness of an ador ad campaign, again, in a manner similar to that done by Yahoo!, buton a more useful subgroup.

[0167] In yet another variation involving a recommender system, auser-rating matrix for items could be computed based on identifyingratings supplied by trend setters and trend predictors identifiedthrough the present invention. It can be seen that the user-trendsetterrating matrix shown in FIG. 2B has a form similar to that described inthe user-item rating matrix in the article by Melville et al. above.

[0168] The latter suggests using content filtering to populate suchmatrix when there are no ratings from a user for a particular item, tosolve the so-called sparse matrix and first-rater problem. The Melvilleauthors postulate that if the user-rating matrix is fully populated,this leads to better predictions and recommendations. The pseudo ratingsused to fill in the user-rating matrix are thus combined with actualratings from the user to arrive at a recommendation, using what theycall a “content-boosted” collaborative filtering algorithm.

[0169] In lieu of the pseudo-ratings for items that are based on theuser's own selections, a recommender system in accordance with thepresent invention can use pseudo-ratings for items which are derivedfrom trendsetter or trend predictor ratings for items, or, at least, forrelatively new items. The latter ratings, of course, could be gleanedvery easily using a basic averaging calculation across the universe oftrendsetters or trend predictors who have actually rated the item. Thenegative ratings, or rejections made by trend setters could also beincorporated.

[0170] In this fashion, a trendsetter “boosted” collaborative filteringsystem can be implemented, instead of using a pure content boostedapproach. Moreover it may be desirable, for example, to still use thecontent-based pseudo ratings from Melville for those items that arerelatively old. Thus, a combination or hybrid approach for generatingpseudo ratings for a user-item rating matrix can be effectuated usingthe present invention.

[0171] The benefit of such approach is that it has the effect ofassociating or causing new users to be associated (or artificiallyneighbored within the user's cluster in a CF sense) with trend settersor trend predictors. This, in turn, means that new items rated by trendsetters or trend predictors will be brought to the “conscious” of therecommender system more rapidly, and thus an overall learning rate fornew material should correspondingly improve. Furthermore, since trendsetters and trend predictors are drawn from a set of persons who tend tomirror the population's overall behavior at a later time, there islittle risk in artificially inducing a learning error. Accordingly,based on conventional metrics for evaluating the performance of aprediction algorithm, the present approach should improve a sensitivityand specificity rating, because the pseudo ratings are based on ratingsthat are likely to be adopted by the new users based on an analysis ofhistorical data (i.e., the predictive value provided by trend setters).

[0172] Other uses for trend setters and trend predictors within arecommender system will be apparent from the above, and the presentinvention is not limited in this respect.

[0173] Trend Laggards/Rejecters

[0174]FIG. 4 is a time chart illustrating a typical adoption rate of anew item within an online community, identifying particular regionswhere subscribers behave as early adopters, middle adopters and lateadopters. This last category, which may be described as “trend laggards”may also be useful to identify as well, for a variety of reasons.

[0175] First, the prevalence of an item in sufficient quantities withina set of trend laggards may indicate the end of a useful adoption cyclefor such item. In other words, the item is likely to not experiencefurther adoption by existing members, and it may not be worth furtheradvertising and/or marketing efforts. Other uses for the trend laggardswill be apparent to those skilled in the art. Again, identifying thetrend laggards can be done using techniques similar to those describedabove for the trend setters.

[0176] The selection and manner of advertising might also bedifferentiated to subscribers, depending on whether they are identifiedas early, middle, or late adopters of items.

[0177] Moreover, in a similar fashion it should be apparent that anotherclass of subscribers, who can generally be described as trend rejecters,can be determined by the present invention. Every community will includesome percentage of persons who for some reason or another, haveattitudes, tastes, behaviors that run counter to the norm, and it may beuseful to identify such persons as well. One manner in which they can bedetermined is by comparing a set of items that are rejected by thetrendsetters, and then evaluating which persons in the community tend torate the rejected trendsetter items highest.

[0178] Thus if trend laggards (and/or trend rejecters) can beidentified, their contributions or weightings to a recommender systemmight be adjusted in a similar manner to that provided for trendsetters,except in the opposite manner. That is, trend laggards (and/or trendrejecter) selections or behaviors might be reduced in weighting within arecommender system, as a way of giving better (or more current)recommendations to the average subscriber.

[0179] The present invention, therefore, affords a mechanism foridentifying and characterizing members in accordance with their adoptiontimes for certain items. Of course, if it is desirable or interesting tolook at adoption time frames other than “early” or “late” this can alsobe done using the present invention to identify such types of persons.It will be apparent to those skilled in the art that the chart of FIG. 4is merely an example, and that the actual demand curves for a particularitem may vary significantly from that shown without deviating from theteachings of the present invention.

[0180] Item Predictors

[0181] In another variation of the invention, it is possible, in someinstances that certain items can themselves act as a type of trendpredictor. For instance in traditional content filtering systems,correlations are often made between items, without regard to theircharacteristics. An example of this is illustrated in commercialrecommender systems used by Amazon and TiVo, which, for instance, willrecommend a second item based on the user's selection of a first item,based on the fact that two items are often selected together by otherusers.

[0182] These systems thus work in part by using the correlation betweentwo items using a Bayesian algorithm, such that when a person selects A,the system recommends B as well based on the fact that a large number ofpersons who have selected A also pick B at some point in time. Thus,these types of correlations also provide a degree of behavioralmeasurement for an online community.

[0183] Another way to look at these kinds of correlations is to noticethat certain items, even if they are not necessarily popular communitywide, can nonetheless act in some instances as predictors for otherrelated items. Thus, for example, an obscure movie title might be highlycorrelated to a more popular title within the adoption profiles of alarge population base. In this respect, therefore, it can be said thatthe obscure item acts as a type of signature, marker or predictor of thepotential for a more popular item. While a single item by itself may notbe sufficiently correlated to suggest all by itself that another item islikely to be popular, it is possible to group a sufficient number ofobscure items in a fashion that may provide predictive value.

[0184] For example, a certain item A may be present 90% of the time withan item X, and have little correlation to any other item, including anyother popular item. Note that X is not necessarily highly correlated toA, however. Another relatively obscure item B may have a similar highcorrelation to item X. A and B may also be highly correlated to otherpopular items.

[0185] Thus if A and B have a very low prevalence rates and yet theytend to be associated with relatively popular items at a rate muchgreater than other low prevalence items, they can behave or act as aform of trend predictor by virtue of the fact that they lead to therecommendation and/or adoption of popular items.

[0186] Accordingly, within a population of online members, suppose thata new product Y is introduced, and A and B both become rapidlycorrelated with Y. One type of prediction can be made to suggest that Yis also likely to become a popular item as well, since A and B arerelatively good markers for predicting the success of items they arerelated to.

[0187] A preferred process for identifying a set of trend predictor“items” therefore is shown in FIGS. 5A and 5B.

[0188] In FIG. 5A, a set of items selected by a group of adopters Y1,Y2, . . . etc. is shown. As can be seen there, X is very popular, andboth A and B are highly correlated to X, even though the latter enjoys agreater correlation perhaps with other items.

[0189] In FIG. 5B, a flowchart is given for the process of identifyingtrend predictor items. At step 510, a set of popular items isidentified, in the same manner as discussed above. At step 520,non-popular items that are highly correlated to popular items are thenidentified. At step 530, the other correlations of the non-popular itemsare also examined, to isolate a particular set of items that will serveas useful predictors and markers;—i.e., they are highly correlated topopular items, and not to obscure items.

[0190] At step 540, the overall predictive value of the item iscalculated, based on examining how many popular items it is associatedwith, the degree of correlation, and the degree of popularity of theitem. Again, the calculation can be based on a matrix type approach asnoted above using conventional methods, and normalized as desired toyield a trend predictor value for each of the potential trend setteritems.

[0191] Thus, at step 550, the set of trend predictor items is created,preferably in an ordered list, so that the top trend predictor items areidentified in sequence. A report of the same can be generated at step560.

[0192] The benefit of knowing the set of trend setter items is that theycan, of course, be used to some extent to identify trendsetters as well.In cases where an e-commerce operator does not have first hand access tothe data selections by particular members, the limited knowledge of theexistence of the relatively obscure but meaningful item selectionswithin a user profile can be used to identify trendsetters withinanother population.

[0193] Furthermore, to some extent, the trend predictor items themselvesmay be useful for conducting another type of item popularity prediction.Thus, at step 570, if items A, B, C are trend predictor items, a searchis made for locating new (recent) adoptions in which all (or subsets) ofA, B, C are present. Based on these results, a report is generated atstep 580 to identify such potentially popular new items.

[0194] Again, in some cases it may be desirable to run both types ofprediction reports, i.e., based on both trend predictor person ratings,and trend predictor item ratings, to compare the results and see whichones provide more accurate evaluations over time for a particularcommunity. Other variations will be apparent to those skilled in theart.

[0195] Other Variations of the Invention

[0196] While the preferred embodiment is directed to studies andidentifications of trendsetters in online based communities, the presentinvention is not limited in this respect. A number of other entities andbusiness operations can benefit from the present invention. For example,a service operated by TiVo is known to monitor selections and behaviorof its subscribers, by observing their selections as made on a localclient device within the subscriber's home. Thus, such service can beused to see which subscribers tend to be good predictors of popularprogramming, by observing, collecting and tabulating programmingselections to identify trendsetters and trend predictors. Thetrendsetter and trend predictor lists for a content programming servicesuch as TiVo are also valuable commodities which can be sold andexchanged with other commercial entities. It will be apparent to thoseskilled in the art that the present teachings could be employed in suchenvironments as well, since the data collection for subscribers can beexamined in a manner that allows for identification of trendsetters andtrend predictors as noted above.

[0197] Similarly, a communications service provider (AT&T for example)could use the present invention to observe the behavior of cell phoneusers, to identify the existence of trendsetters within such population.For instance, such service could monitor which subscribers are the firstto use various features offered by the service, such as special callingfunctions, email functions, etc. This same process could be employed bya number of other consumer and business electronic equipment providersto better glean the demographics, needs and interests of theirpurchasing base.

[0198] In yet another application, the invention could be employed bysoftware vendors to observe and identify purchasers who are trendsetterswith respect to the vendors' products. For example, a company such asMicrosoft could see which customers are the first to use or exploit newfunctions and features provided in a commercial software package, oroperating system package. A content provider such as Yahoo! could usethe invention to monitor which subscribers are the first to look atcertain types of contents or online functions that are made available innew releases.

STRUCTURE OF THE PREFERRED EMBODIMENT

[0199] A preferred embodiment of a trendsetter identification and demandprediction system 600 constructed in accordance with the presentinventions is illustrated in FIG. 6. The system is composed of severalcomponents including a Network 602, through which a number of separateNetwork Connections 604 are provided to a Service Provider System(preferably a Server Device) 620 by a plurality of Customer NetworkDevices 612. It will be understood by those skilled in the art thatother components may be connected to Network 602, and that not allconnections shown need to be active at all times.

[0200] There are also several software components and electronicdatabases associated with the aforementioned network-connected devices,including a Subscriber Traffic module 621, a Subscriber ProfileModule/Database 622, a Recommender module 623, a Search Engine module624, a Trendsetter—Trend predictor database 625, a Subscription Adoptiontable database 626, an Item predictor database 627, an AdvertisingDelivery system 628, and an Item profile database 629. Some of thesesoftware components of course are essentially the same as those found ina prior art system, except they may be modified appropriately tocooperate with the new software components of the present invention.

[0201] Network 602 is preferably the Internet, but could be anyimplemented in any variety of commonly used architectures, includingWAN, LAN, etc. Network Connections 604 are conventional dial-up and/ornetwork connections, such as from analog/digital modems, cable modems,satellite connections, etc., between any conventional network device andan Internet Service Provider in combination with browser software suchas Netscape Navigator, Microsoft Internet Explorer or AOL. In asatellite media distribution system implementation, Client Device 612 isa satellite receiver, a TiVo receiver, or the like, and an interface toa service provider does not require a browser.

[0202] In most applications, Customer Network Device 612 will betypically desk top computers, laptop computers, personal digitalassistants (PDAs), cell phones, or some form of broadcast receiver(cable, satellite, DSL). Server Network Device 610 is typically anetwork server supporting a service provider website, which, again, maybe comprised of a set of logically connected and linked web pagesaccessible over the Internet. Of course, other structures andarchitectures may be more suitable on a case by case basis for anyparticular implementation of the present inventions, and so the presentinventions are not limited in this respect.

[0203] Software elements of the present invention typically will becustom tailored for a particular application, but preferably willinclude some common features, including the following.

[0204] Operating on System Network Device 610 are the following softwareroutines and/or supporting structures, which implement a form of mediadistribution.

[0205] First, a Subscriber traffic monitor module 621 observessubscriber behavior, including explicit and implicit data input. Thus itlogs subscriber activity, such as queries, page views, item adoptions,etc. as noted above.

[0206] A Subscriber Profile Module/Database 622 analyzes subscriberinputs, queries, title selections, title deliveries, etc., and forms acustomized interest profile for each subscriber. This can be done inusing any conventional method. This customized subscriber-specificinformation is in addition, of course, to any other basiccustomer-specific information that may be maintained, such as authorizeduser names, account numbers, physical addresses, credit cardinformation, etc.

[0207] Based on such information in a subscriber profile, a Recommendermodule 623 operates to provide suggestions for items that are likely tobe of interest to the subscriber. These can also be provided within astandard query interface presented by a Search Engine module 624. Again,a variety of such types of recommender systems are well-known in the artand can be incorporated within embodiments of the present invention. Theitem suggestions may be provided while the user is engaged in aninteractive session across network 602, or, even while the user is notconnected to Service Device 610. The benefit of the latter feature, ofcourse, is that a subscriber delivery queue can be updated even withoutdirect ongoing participation by the user, who may be too busy to engagein a session to locate items. As noted above, Recommender module 623 maygenerate recommendations that are influenced by the trendsetters andtrend predictors in accordance with the discussion above.

[0208] A Search Engine module 624 again works in a conventional fashionto retrieve content, materials and results from the service providersite, or other websites, in response to user queries. Profile orcataloguing information for items of interest to the subscribers may beorganized in an Item Profile database 629. This item profile informationmay be searchable by subject matter, category, genre, tide, artist andother attributes as determined by subscriber interests, systemadministrative requirements, the nature of the item in question, etc.Search Engine module 624 also presents a query interface to subscribersto allow them to peruse and view information about the media items.Again, as noted above, Search Engine module 624 may generate resultsthat are influenced by the trendsetters and trend predictors inaccordance with the discussion above.

[0209] An Advertising delivery module 628 is responsible for deliveringadvertising to the subscribers, including the trend predictors, inaccordance with the techniques described above. Furthermore, asdiscussed above, Advertising delivery module 624 may also generateadvertising that is directly influenced by the trendsetters and trendpredictors in accordance with the discussion above.

[0210] A trendsetter—trend predictor module 625 basically functions inaccordance with the processes described above in connection with FIGS.1-4. Based on such operation, a trendsetter—trend predictor database iscreated to include the type of data noted above as well. The trendsetterdatabase is derived, as noted above, from examining Subscriber AdoptionTables 626. This module is also used, as noted earlier, to generateprediction results for demand for new items as may be requested by theservice provider, and to identify trend laggards and/or trend rejectersas may be requested.

[0211] Finally, an item predictor module/database 627 operates inaccordance with the description given above for FIGS. 5A and 5B.

[0212] It will be apparent to those skilled in the art that this is notthe entire set of software modules that can be used, or an exhaustivelist of all operations executed by such modules. It is expected, infact, that other features will be added by system operators inaccordance with customer preferences and/or system performancerequirements.

[0213] Furthermore it will be apparent to those skilled in the art thata service provider system implementing the present invention may notinclude all of the modules/databases as noted above, depending on theneeds, requirements or desires of its subscribers, and other technicallimitations. For example, many websites do not require a recommendersystem, because they do not provide such functionality to theirsubscribers. Thus, the invention is not limited to the preferredembodiments noted above. Finally, while not explicitly shown ordescribed herein, the details of the various software routines,executable code, etc., required to effectuate the functionalitydiscussed above in such modules are not material to the presentinvention, and may be implemented in any number of ways known to thoseskilled in the art based on the present description.

[0214] It will be understood by those skilled in the art that the aboveis merely an example of a trendsetter identification and tabulationsystem/method and that countless variations on the above can beimplemented in accordance with the present teachings. A number of otherconventional steps that would be included in a commercial applicationhave been omitted, as well, to better emphasize the present teachings.

[0215] The above descriptions are intended as merely illustrativeembodiments of the proposed inventions. It is understood that theprotection afforded the present invention also comprehends and extendsto embodiments different from those above, but which fall within thescope of the present claims.

What is claimed is:
 1. A method of identifying demand by an onlinecommunity for a particular item, the method comprising the steps of: (a)identifying early adopter members of the online community, which earlyadopter members are characterized by a demand behavior for items whichleads and is imitated by other members of the online community; (b)measuring an acceptance value for the particular item by such earlyadopter members of the online community.
 2. The method of claim 1,further including a step: generating a demand score identifying apredicted overall remaining demand for the particular item.
 3. Themethod of claim 2, wherein said demand score reflects a predictedoverall remaining demand for consumers of the particular item outside ofthe online community.
 4. A method of identifying demand by an onlinecommunity for a particular item, the method comprising the steps of: (a)identifying late adopter members of the online community, which lateadopter members are characterized by a demand behavior for theparticular item which lags other members of the online community; (b)measuring an acceptance value for the particular item by such lateadopter members of the online community; (c) generating a demand scoreidentifying a predicted overall remaining demand for the particularitem.
 5. The method of claim 4, wherein said demand score reflects apredicted overall remaining demand for consumers of the particular itemoutside of the online community.
 6. A method of identifying predicteddemand for an item, the method comprising the steps of: (a) identifyinga group of trend predictor members within an electronic onlinecommunity; (b) identifying one or more items rejected by said group oftrend predictor members, by monitoring their activities duringelectronic data collection sessions; (c) compiling a list of said onemore rejected items.
 7. The method of claim 6, further including a step(d): identifying trend rejecter members within said electroniccommunity, said trend rejecters being characterized as members who haveadopted a substantial number of said one or more rejected items.
 8. Themethod of claim 7, further including a step (d): calculating an itemrejection prevalence rate among said trend predictor members for each ofsaid one or more items.
 9. The method of claim 8, further including astep (e): disseminating advertising to said trend predictor members forat least one selected rejected item; and a step (f): measuring anychange in an item rejection prevalence rate for said at least oneselected rejected item.
 10. A method of identifying potential demand fora new product and/or new service within a market, the method comprisingthe steps of: (a) identifying one or more products and/or services whichare characterized as having achieved substantial economic success withinthe market; (b) identifying early adopters of said one or more productsand/or services within an electronic community; (c) monitoring theactivities of said early adopters during electronic data collectionsessions, to determine a prevalence rating of their collectiveimpressions of the new product and/or new service.
 11. The method ofclaim 10, wherein step (c) is performed before the new product and/ornew service is introduced into the market.
 12. The method of claim 10,further including a step (d): presenting advertising materials to saidearly adopters, and measuring a change in said prevalence rating.
 13. Amethod of identifying trend predictor items within a set of itemsavailable to an online community, the method comprising the steps of:(a) identifying a first item having a relatively high adoption rate; (b)calculating a correlation between said first item and one or more seconditems which have a relatively low adoption rate; (c) identifying atleast one second item which is highly correlated to said first item, andnot highly correlated to other of said one or more second items; (d)repeating steps (b) and (c) to identify a first set of first items whichhave relatively high adoption rates, and a second set of second itemswhich are highly correlated to said first set of first items.
 14. Themethod of claim 13, further including a step: (e): measuring acorrelation of said second set of items to a newly introduced item togenerate a prediction of a demand for said newly introduced item.
 15. Asystem for identifying demand by an online community for a particularitem, the system comprising: (a) a first software module executing onfirst computing device, said first software module being configured for:i) identifying early adopter members of the online community; whereinsaid early adopter members are characterized by a demand behavior foritems which leads and is imitated by other members of the onlinecommunity; ii) storing profiles for said early adopter members in atrendsetter database; iii) measuring an acceptance value for theparticular item by such early adopter members of the online community.16. The system of claim 15, wherein said first computing device is anInternet based sever supporting a website maintained by an e-commerceoperator.
 17. The system of claim 15, wherein the particular item is aproduct.
 18. The system of claim 15, wherein the early adopter membersare represented by web pages.
 19. The system of claim 15, wherein theearly adopter members are extracted from purchasing records ofindividuals having a common geographic demographic.
 20. The system ofclaim 15, wherein the members of the online community are ranked byadoption date for items to identify said early adopter members.